Abstract. Satellite remote sensing has wide applications in many fields. However, the quality of the observed images captured from the satellite sensors exhibits significant variances and most images are low resolution. Therefore, they adversely affect the system performance in a variety of real-world applications such as object recognition and analysis. In order to enhance the resolution of remote sensing images, we propose a super-resolution neural network called progressive residual depth neural network (PRDNN). The progressive residual structure used by PRDNN can gradually discover the feature information of satellite images at different levels and different receptive fields, thus providing more detailed features for reconstructing super-resolution satellite images. The experimental results of the DOTA satellite image database demonstrate that the proposed method is superior to the most advanced super-resolution algorithm in recent years.

中文翻译：

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基于渐进残差深度神经网络的卫星图像超分辨率

摘要。卫星遥感在许多领域都有广泛的应用。然而，从卫星传感器捕获的观察图像的质量表现出显着差异，并且大多数图像分辨率较低。因此，它们对各种实际应用（例如对象识别和分析）中的系统性能产生不利影响。为了提高遥感图像的分辨率，我们提出了一种称为渐进残差深度神经网络（PRDNN）的超分辨率神经网络。PRDNN采用的渐进残差结构可以逐步发现卫星图像不同层次、不同感受野的特征信息，从而为重建超分辨率卫星图像提供更细致的特征。